Semester:
7th
Course Type:
Elective Specialization courses (ΠΜ-E)
Track:
-
Code:
ΕΠ23
ECTS:
6
TEACHING HOURS per week
Theory:
4
Seminar:
-
Laboratory:
-
Specializations
Foundations of Computer Science (S1):
-
Data and Knowledge Management (S2):
-
Software (S3):
-
Hardware and Architecture (S4):
-
Communications and Networking (S5):
-
Signal and Information Processing (S6):
-
Related Courses
Detailed Description
Course Content
- Mathematical foundations of computer vision.
- Projective geometry, image formation, and stereoscopy.
- Basic concepts of digital images and neighborhood (local) operations.
- Feature extraction and edge detection.
- Corner detection and multi-scale pyramids.
- Scale space, blob detectors, and local feature descriptors.
- Advanced local features and vector representations of images.
- Basic concepts of machine learning.
- Introduction to traditional image recognition.
- Optical flow.
- Introduction to neural networks and the PyTorch library.
- Convolutional Neural Networks.
- The attention mechanism and the Transformer architecture.
- Object detection.
- Semantic image segmentation.
- Self-supervised representation learning and vision-language representations.
- Image synthesis with generative modeling.
LITERATURE AND STUDY MATERIALS - READING LIST
- Computer Vision, 2nd Edition, R. Szeliski, Ed. Fountas, 2022 (Greek).
- Machine Learning, K. Diamantaras and D. Botsis, Ed. Kleidarithmos, 2019.
- Image Processing and Analysis, G. Tziritas and N. Komodakis, Kallipos, 2023.
e-Class link